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Duration 14 hours
Course Outline
Introduction to CANN and Ascend AI Processors
- What is CANN? Its function within Huawei’s AI computing stack
- An overview of Ascend processor architecture (including 310, 910, etc.)
- A summary of supported AI frameworks and the associated toolchain
Model Conversion and Compilation
- Utilizing the ATC tool for model conversion from TensorFlow, PyTorch, and ONNX
- Generating and verifying OM model files
- Addressing unsupported operators and resolving typical conversion problems
Deployment with MindSpore and Other Frameworks
- Deploying models using MindSpore Lite
- Integrating OM models via Python APIs or C++ SDKs
- Working effectively with the Ascend Model Manager
Performance Optimization and Profiling
- Comprehending AI Core, memory, and tiling optimizations
- Profiling model execution using CANN tools
- Best practices for enhancing inference speed and resource utilization
Error Handling and Debugging
- Identifying and resolving common deployment errors
- Interpreting logs and utilizing the error diagnosis tool
- Conducting unit tests and functional validation of deployed models
Edge and Cloud Deployment Scenarios
- Deploying to Ascend 310 for edge-based applications
- Integrating with cloud-based APIs and microservices
- Real-world case studies in computer vision and NLP
Summary and Future Steps
Requirements
- Practical experience with Python-based deep learning frameworks, such as TensorFlow or PyTorch
- A solid understanding of neural network architectures and model training workflows
- Fundamental proficiency with Linux CLI and scripting
Target Audience
- AI engineers focused on model deployment
- Machine learning practitioners aiming for hardware acceleration
- Deep learning developers creating inference solutions
